Monthly streamflow forecasting at varying spatial scales in the Rhine basin

Schick, Simon; Rößler, Ole; Weingartner, Rolf

doi:10.5194/hess-22-929-2018

Cited by 13 publications

(7 citation statements)

References 35 publications

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“…For the statistical prediction of drought, predictors are generally obtained from historical observations (or reanalysis) that are already known prior to the prediction period. With advances in the weather and climate forecast, predictors may also be obtained from dynamical forecast for the prediction of hydroclimatic variables (Chowdhury & Sharma, 2009;Foster & Uvo, 2010;Lang & Wang, 2010;Marcos et al, 2017;Sahu et al, 2016;Schick et al, 2017;Stephenson et al, 2005). This is also related to the Model Output Statistics (MOS) of climate forecast, which will be introduced afterward in section 5.1.2.…”

Section: Selection Of Predictorsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

428

232

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Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

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Section: Selection Of Predictorsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

428

232

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“…Examples of operational services based on the dynamic approach include the Australian Bureau of Meteorology's dynamic modelling system (Laugesen et al, 2011;Tuteja et al, 2011;Lerat et al, 2015); the Hydrological Ensemble Forecast Service (HEFS) of the US National Weather Service (NWS) Demargne et al, 2014); the Hydrological Outlook UK (HOUK) (Prudhomme et al, 2017); and the short-term forecasting European Flood Alert System (EFAS) (Cloke et al, 2013). Examples of operational services based on a statistical approach include the Bureau of Meteorology's Bayesian Joint Probability (BJP) forecasting system (Senlin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

Woldemeskel

McInerney

Lerat

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.

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“…Slater and Villarini, 2018). This approach is similar to that of model output statistics (MOS) long used by the weather forecasting community (Glahn and Lowry, 1972), but also in seasonal hydrological predictions (Schick et al, 2018). For instance, if a climate model tends to overpredict winter rainfall, this bias is accounted for directly in the streamflow predictions, given that the model is trained using the same winter rainfall forecasts (assuming a constant bias).…”

Section: Model Performance and Bias Minimizationmentioning

confidence: 99%

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

Slater

Arnal²,

Boucher³

et al. 2022

Preprint

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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

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Monthly streamflow forecasting at varying spatial scales in the Rhine basin

Cited by 13 publications

References 35 publications

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

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